library(tidyverse)
library(lubridate)
library(forecast)
library(ggrepel)
library(rvest)
library(plotly)
library(xtable)

dja <- read_csv("data/DJA.csv",skip = 4)
gold <- read_csv("data/GOLD_1791-2018.csv",skip = 3)
interest_rate <- read_csv("data/INTERESTRATE_1857-2018.csv",skip = 1)
sap <- read_csv("data/SAP_1871-2018.csv", skip=1)
cpi <- read_csv("data/USCPI_1774-2018.csv", skip=4)
gdp <- read_csv("data/USGDP_1790-2018.csv", skip=2)
wage <- read_csv("data/USWAGE_1774-2018.csv", skip=3)
gdp_uk <-  read_csv("data/UKGDP_1700-2017.csv", skip=1)
brit_gold <- read_csv("data/GOLD_brit_1257-1945.csv", skip=1)
gold_london <- read_csv("data/GOLD_london_1718-2017.csv", skip = 1)

dja <- dja %>% 
  mutate(Date = parse_date_time(Date,orders = "mdy"))
ggplotly(ggplot(dja,aes(Date, DJIA))+
  geom_line()) %>% 
  layout(legend = list(
      orientation = "h"))

summary(dja)

      Date                          DJIA         
 Min.   :1885-05-02 00:00:00   Min.   :   24.36  
 1st Qu.:1916-02-09 06:00:00   1st Qu.:   70.41  
 Median :1946-10-14 12:00:00   Median :  199.28  
 Mean   :1949-03-11 07:36:39   Mean   : 2354.03  
 3rd Qu.:1982-06-06 06:00:00   3rd Qu.: 1002.07  
 Max.   :2018-09-10 00:00:00   Max.   :26616.71  

dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) %>% 
ggplot(.,aes(Date, dif))+
  geom_rect(fill="firebrick", 
            xmin=parse_date_time("01-01-1930",orders = "mdy"),
            xmax=parse_date_time("01-01-1940",orders = "mdy"),
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line()

url <- "https://www.caproasia.com/2016/04/12/economic-crisis-since-1900-2015/"
crisis <- url %>%
  read_html() %>% 
  html_nodes(css = 'table') %>% 
  html_table(header = T)
crisis <- crisis[[1]] %>% 
  filter(Affected %in% c("United States","Global")) %>% 
  separate(Period,c("desde","hasta")," – ")

Expected 2 pieces. Missing pieces filled with `NA` in 10 rows [1, 4, 5, 6, 7, 8, 9, 10, 11, 12].

#en realidad las que terminan en "s" no duran toda la década. Las agrego a mano.
  # mutate(hasta = parse_date_time(case_when(grepl("s",desde)~as.numeric(str_extract(desde,"[[:digit:]]*"))+10,
  #                          TRUE~ as.numeric(hasta)),"y"),
  #        desde = parse_date_time(str_extract(desde,"[[:digit:]]*"),"y"))
crisis <- crisis %>% 
  mutate(hasta = parse_date_time(case_when(desde=="1970s"~"1979",
                           desde=="1980s"~"1982",
                           desde == "1990s"~"1991",
                           TRUE~hasta),"y"),
         desde = parse_date_time(case_when(desde=="1970s"~"1973",
                           desde=="1980s"~"1981",
                           desde=="1990s"~"1990",
                           TRUE~desde),"y"))

# tabla <- crisis[[1]] %>% 
#   filter(Affected %in% c("United States","Global")) %>% 
#   select(-Region, - Affected) %>% 
#   xtable(.)
#En la consola
# print(tabla, include.rownames = F)

crisis_largas <- na.omit(crisis)
crisis_puntuales <- crisis %>% 
  filter(is.na(hasta))
dja <- dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) 
ggplot()+
  geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line(data = dja,aes(Date, dif))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")

NA

gold %>% 
  ggplot(., aes(Year, `New York Market Price (U.S. dollars per fine ounce)`))+
  geom_line(size=1)+
  geom_vline(xintercept = 1971, color = "red")+
  geom_label_repel(data=data_frame(),aes(x=1971,y=1000,label="Fin del patrón oro"),nudge_x = -5,force=10,size=7)+
  theme_minimal()+
  labs(y="Dólares por onza de oro", x="Año", title="Precios del oro por onza en el mercado de Nueva York")+
  theme(text = element_text(size = 20))
ggsave("plots/oro.png", dpi=300, width = 10, height = 7, scale=1)

graf <- interest_rate %>% 
  gather(type,rate,2:4) %>% 
  ggplot(., aes(Year,rate,color=type))+
  geom_line()+
  guides(color=guide_legend(nrow=2,byrow=TRUE))+
  theme(legend.position = "bottom")
ggplotly(graf) %>%
  layout(legend = list(
      orientation = "h"
    )
  )

sap %>% 
  summary()

      Year      The S&P Index Average for January  Annual Yield  
 Min.   :1871   Min.   :   3.240                  Min.   :1.140  
 1st Qu.:1908   1st Qu.:   7.433                  1st Qu.:3.310  
 Median :1944   Median :  16.430                  Median :4.500  
 Mean   :1944   Mean   : 261.888                  Mean   :4.356  
 3rd Qu.:1981   3rd Qu.: 122.058                  3rd Qu.:5.390  
 Max.   :2018   Max.   :2791.730                  Max.   :8.710  
                                                  NA's   :1      
 The Accumulated S&P Index Average for January
 Min.   :     1.0                             
 1st Qu.:    11.5                             
 Median :   122.0                             
 Mean   : 21442.8                             
 3rd Qu.:  5360.0                             
 Max.   :298189.7                             
                                              

sap %>% 
  gather(type, value,2:4) %>%
  mutate(type= case_when(type=="The S&P Index Average for January"~"The S&P Index\nAverage for January",
                         type=="The Accumulated S&P Index Average for January"~"The Accumulated S&P\nIndex Average for January",
                         TRUE~type)) %>% 
  ggplot(.,aes(Year,value, color=type))+
  geom_line()+
  facet_grid(type~.,scale="free")+
  theme(legend.position = "bottom",
        strip.text.y = element_text(angle = 0))

ts(sap$`Annual Yield`, start=min(sap$Year), frequency = 1) %>% 
  na.omit() %>%
  auto.arima(.)

Series: . 
ARIMA(0,1,2) 

Coefficients:
          ma1      ma2
      -0.0141  -0.3007
s.e.   0.0802   0.0810

sigma^2 estimated as 0.4111:  log likelihood=-141.37
AIC=288.73   AICc=288.9   BIC=297.68

cpi %>% 
  ggplot(., aes(Year, `U.S. Consumer Price Index *`))+
  geom_line(size=1)+
  geom_vline(xintercept = 1971, color = "red")+
  geom_label_repel(data=data_frame(),aes(x=1971,y=100,label="Fin del patrón oro"),nudge_x = -5,force=10,size=7)+
  theme_minimal()+
  labs(y="IPC", x="Año")+
  theme(text = element_text(size = 20))
ggsave("plots/cpi_orig.png", scale = 1)

Saving 7.29 x 4.5 in image

gdp %>% 
  ggplot(., aes(Year, `Real GDP (millions of 2012 dollars)`))+
  geom_line(size=1)+
  # geom_vline(xintercept = 1971, color = "red")+
  # geom_label_repel(data=data_frame(),aes(x=1971,y=100,label="Fin del patrón oro"),nudge_x = -5,force=10,size=7)+
  theme_minimal()+
  labs(y="Real GDP", x="Año")+
  theme(text = element_text(size = 30))
ggsave("plots/PBI.png", scale = 1)

Saving 7.29 x 4.5 in image

gdp <- left_join(gold, gdp, by = "Year") %>% 
  mutate(gdp_in_gold = `Nominal GDP per capita (current dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) 
  
ggplotly(ggplot(gdp,aes(Year,gdp_in_gold))+
  geom_line())

library(scales) # to access breaks/formatting functions
ggplot()+
geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-Inf,
            ymax=Inf,
            alpha=0.5)+
  geom_line(size=1,
    data = gdp %>% 
              filter(Year>parse_date_time(1900,"y"))
            ,aes(Year, gdp_in_gold))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")+
  geom_vline(xintercept = parse_date_time(1971,"y"),color = "gold")+ #fin del patron oro
  scale_x_datetime(date_breaks = "15 years",labels = date_format("%Y") )+  
  theme_minimal()+
  labs(x="", y="PBI en oro", title="PBI Estados Unidos",
       subtitle= "Millones de onzas de oro, 1900-2017")+
  theme(text = element_text(size = 20))
ggsave("plots/gdp_in_gold_eda.PNG", dpi = 300, width = 10,height = 6)

wage %>% 
  summary()

      Year      Costs of Unskilled Labor (index 1860 = 100)
 Min.   :1774   Min.   :   31.00                           
 1st Qu.:1835   1st Qu.:   79.65                           
 Median :1896   Median :  143.50                           
 Mean   :1896   Mean   : 2499.49                           
 3rd Qu.:1956   3rd Qu.: 1682.75                           
 Max.   :2017   Max.   :19640.40                           
                                                           
 Production Workers Hourly Compensation (nominal dollars)
 Min.   : 0.020                                          
 1st Qu.: 0.060                                          
 Median : 0.150                                          
 Mean   : 3.906                                          
 3rd Qu.: 2.555                                          
 Max.   :32.390                                          
 NA's   :16                                              

ggplotly(
wage %>% 
  left_join(cpi,by="Year") %>% 
  na.omit() %>% 
  mutate(salario_horario_real = `Production Workers Hourly Compensation (nominal dollars)`/`U.S. Consumer Price Index *`) %>% 
  ggplot(.,aes(Year,salario_horario_real))+
  geom_line() 
  )

wg_gold <- wage %>% 
  filter(Year>=1900) %>% 
  left_join(gold, gdp, by = "Year") %>% 
  mutate(wg_in_gold = `Production Workers Hourly Compensation (nominal dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) %>% 
  na.omit()  
ggplot()+
geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-Inf,
            ymax=Inf,
            alpha=0.5)+
  geom_line(size=1,data = wg_gold, aes(Year, wg_in_gold))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")+
  geom_vline(xintercept = parse_date_time(1971,"y"),color = "gold")+ #fin del patron oro
  scale_x_datetime(date_breaks = "15 years",labels = date_format("%Y") )+
  theme_minimal()+  
  labs(x="", y="Salario en oro", title="Salario horario Estados Unidos",
       subtitle="Onzas de oro, 1900-2017")+
  theme(text = element_text(size = 20))
ggsave("plots/wg_in_gold_eda.PNG", dpi = 300, width = 10,height = 6)

oro <- full_join(brit_gold,gold_london)

Joining, by = "Year"

names(oro)

[1] "Year"                                                                                  
[2] "British Official Price (British pounds per fine ounce end of year)"                    
[3] "London Market Price (British £ [1718-1949] or U.S. $ [1950-2011] per fine ounce)"

# de http://fx.sauder.ubc.ca/data.html
tc <- read_csv("data/ex_rate.csv")

Parsed with column specification:
cols(
  `MMM YYYY` = col_character(),
  `GBP/USD` = col_double()
)

library(lubridate)
tc <- tc %>% mutate(date = parse_date_time(`MMM YYYY`,"my"),
              Year = year(date)) %>% 
  group_by(Year) %>% 
  summarise(gbp_usd = mean(`GBP/USD`))
# de http://fx.sauder.ubc.ca/etc/USDpages.pdf
tc_1950_1970 <- data_frame(Year=1950:1970, gbp_usd = 0.35714) %>% 
  mutate(gbp_usd = case_when(Year ==1967 ~ 0.36210,
                             Year >1967 ~ 0.41667,
                             TRUE ~ gbp_usd))
tc <- bind_rows(tc_1950_1970,tc)
tc

tail(oro)

oro$`London Market Price (British £ [1718-1949] or U.S. $ [1950-2011] per fine ounce)`

  [1]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [13]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [25]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [37]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [49]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [61]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [73]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [85]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
 [97]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[109]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[121]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[133]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[145]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[157]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[169]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[181]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[193]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[205]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[217]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[229]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[241]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[253]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[265]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[277]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[289]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[301]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[313]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[325]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[337]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[349]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[361]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[373]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[385]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[397]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[409]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[421]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[433]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[445]      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA      NA
[457]      NA      NA      NA      NA      NA    4.31    4.26    4.31    4.26    4.25    4.26    4.25
[469]    4.25    4.25    4.25    4.28    4.30    4.29    4.26    4.26    4.27    4.26    4.28    4.26
[481]    4.26    4.26    4.25    4.25    4.27    4.28    4.30    4.31    4.29    4.25    4.28    4.26
[493]    4.25    4.26    4.25    4.26    4.27    4.25    4.25    4.25    4.25    4.30    4.33    4.30
[505]    4.35    4.32    4.37    4.26    4.26    4.31    4.34    4.32    4.38    4.37    4.35    4.35
[517]    4.25    4.24    4.23    4.23    4.23    4.23    4.23    4.23    4.23    4.23    4.25    4.25
[529]    4.24    4.23    4.23    4.23    4.23    4.23    4.23    4.23    4.23    4.23    4.23    4.23
[541]    4.23    4.25    4.24    4.26    4.29    4.31    4.34    4.36    4.36    4.42    4.47    4.52
[553]    4.58    4.63    5.19    5.48    5.76    5.21    4.99    4.36    4.33    4.44    4.36    4.25
[565]    4.25    4.24    4.23    4.23    4.24    4.23    4.23    4.23    4.24    4.24    4.25    4.24
[577]    4.24    4.24    4.24    4.24    4.24    4.24    4.25    4.24    4.24    4.24    4.24    4.24
[589]    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24
[601]    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24
[613]    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24
[625]    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.25    4.25
[637]    4.25    4.24    4.24    4.25    4.25    4.25    4.24    4.25    4.25    4.24    4.25    4.25
[649]    4.24    4.25    4.25    4.25    4.24    4.24    4.24    4.24    4.24    4.24    4.24    4.24
[661]    4.24    4.24    4.50    5.65    5.35    4.67    4.51    4.69    4.27    4.25    4.25    4.25
[673]    4.25    4.25    4.63    5.90    6.24    6.88    7.11    7.01    7.04    7.13    7.72    8.40
[685]    8.40    8.40    8.40    8.40    8.40    8.40    8.40    8.40    8.40   34.71   34.71   34.71
[697]   34.71   34.96   35.01   35.00   34.95   35.10   35.09   35.28   35.15   35.10   35.09   35.09
[709]   35.13   35.17   35.19   38.69   41.09   35.94   40.80   58.16   97.32  159.26  161.02  124.84
[721]  147.71  193.22  306.68  612.56  460.03  375.67  424.35  360.48  317.28  367.51  446.47  437.05
[733]  381.43  383.47  362.18  343.73  359.77  384.01  384.16  387.69  331.10  294.16  278.64  279.03
[745]  271.04  309.68  363.32  409.17  444.45  603.77  695.39  871.96  972.35 1224.53 1571.52 1668.98
[757] 1411.23 1266.40 1160.06 1250.74 1257.12

oro <- oro %>% 
  filter(Year %in% c(1700:2017)) %>% 
  mutate(serie_unificada = case_when(Year < 1718 ~ `British Official Price (British pounds per fine ounce end of year)`,
                                     Year >=1718 ~`London Market Price (British &pound; [1718-1949] or U.S. $ [1950-2011] per fine ounce)`))
## Tengo que pasar todo a libras, desde 1950 al serie esta en dólares
oro <- oro %>% 
  left_join(tc) %>% 
  mutate(serie_unificada = case_when(Year>1950 ~ serie_unificada*gbp_usd,
                                     TRUE ~ serie_unificada))

Joining, by = "Year"

  
ggplot(oro, aes(Year, serie_unificada))+
  geom_line()

gdp_uk <- gdp_uk %>% 
  left_join(oro) %>% 
  mutate(gdp_in_gold = `Nominal GDP (million of pounds)`/serie_unificada)

Joining, by = c("Year", "British Official Price (British pounds per fine ounce end of year)", "London Market Price (British £ [1718-1949] or U.S. $ [1950-2011] per fine ounce)", "serie_unificada", "gbp_usd")

crisis <- gdp_uk %>% 
  filter(Year %in% c(1700:1900)) %>% 
  mutate(crisis =gdp_in_gold) %>% 
  filter(Year %in% c(1794,1803, 1812, 1822,1833,1842,1850, 1858, 1868,1879, 1885, 1893))
gdp_uk %>% 
  filter(Year %in% c(1700:1900)) %>% 
ggplot(., aes(Year, gdp_in_gold))+
  geom_line(size=1)+
  geom_text_repel(data = crisis, aes(Year, crisis,label=Year),nudge_x = 5, nudge_y = -20,force=12,size=4)+
  theme_minimal()+
  labs(y="PBI en oro", x="Año", title= "Producto Bruto Interno Reino Unido", 
       subtitle="Millones de onzas de oro. 1700-1900")+
  theme(text = element_text(size = 20))
ggsave("plots/uk_gdp.png",scale = 1)

Saving 7.29 x 4.5 in image

---
title: "Exploratory Data Analysis"
output: html_notebook
---

## load

```{r setup}
library(tidyverse)
library(lubridate)
library(forecast)
library(ggrepel)
library(rvest)
library(plotly)
library(xtable)
```


```{r, message=FALSE}
dja <- read_csv("data/DJA.csv",skip = 4)
gold <- read_csv("data/GOLD_1791-2018.csv",skip = 3)
interest_rate <- read_csv("data/INTERESTRATE_1857-2018.csv",skip = 1)
sap <- read_csv("data/SAP_1871-2018.csv", skip=1)
cpi <- read_csv("data/USCPI_1774-2018.csv", skip=4)
gdp <- read_csv("data/USGDP_1790-2018.csv", skip=2)
wage <- read_csv("data/USWAGE_1774-2018.csv", skip=3)
gdp_uk <-  read_csv("data/UKGDP_1700-2017.csv", skip=1)

brit_gold <- read_csv("data/GOLD_brit_1257-1945.csv", skip=1)
gold_london <- read_csv("data/GOLD_london_1718-2017.csv", skip = 1)
```


## Down Jones Avg
el dja es una serie diaria, todas las demás son anuales.

```{r}
dja <- dja %>% 
  mutate(Date = parse_date_time(Date,orders = "mdy"))

ggplotly(ggplot(dja,aes(Date, DJIA))+
  geom_line()) %>% 
  layout(legend = list(
      orientation = "h"))

summary(dja)
```


```{r}

dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) %>% 
ggplot(.,aes(Date, dif))+
  geom_rect(fill="firebrick", 
            xmin=parse_date_time("01-01-1930",orders = "mdy"),
            xmax=parse_date_time("01-01-1940",orders = "mdy"),
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line()

```

armo una lista de las crisis conocidas

# Crisis

```{r}
url <- "https://www.caproasia.com/2016/04/12/economic-crisis-since-1900-2015/"
crisis <- url %>%
  read_html() %>% 
  html_nodes(css = 'table') %>% 
  html_table(header = T)

crisis <- crisis[[1]] %>% 
  filter(Affected %in% c("United States","Global")) %>% 
  separate(Period,c("desde","hasta")," – ")


#en realidad las que terminan en "s" no duran toda la década. Las agrego a mano.
  # mutate(hasta = parse_date_time(case_when(grepl("s",desde)~as.numeric(str_extract(desde,"[[:digit:]]*"))+10,
  #                          TRUE~ as.numeric(hasta)),"y"),
  #        desde = parse_date_time(str_extract(desde,"[[:digit:]]*"),"y"))

crisis <- crisis %>% 
  mutate(hasta = parse_date_time(case_when(desde=="1970s"~"1979",
                           desde=="1980s"~"1982",
                           desde == "1990s"~"1991",
                           TRUE~hasta),"y"),
         desde = parse_date_time(case_when(desde=="1970s"~"1973",
                           desde=="1980s"~"1981",
                           desde=="1990s"~"1990",
                           TRUE~desde),"y"))


```


```{r}
# tabla <- crisis[[1]] %>% 
#   filter(Affected %in% c("United States","Global")) %>% 
#   select(-Region, - Affected) %>% 
#   xtable(.)

#En la consola
# print(tabla, include.rownames = F)

```


```{r}
crisis_largas <- na.omit(crisis)
crisis_puntuales <- crisis %>% 
  filter(is.na(hasta))


dja <- dja %>% 
  mutate(dif = (DJIA - lag(DJIA, default = DJIA[1]))/lag(DJIA, default = DJIA[1])) 
ggplot()+
  geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-1,
            ymax=1,
            alpha=0.5)+
    geom_line(data = dja,aes(Date, dif))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")
  

```


Este gráfico me da la sensación de que todo estuviera corrido a la derecha (mirando las crisis puntuales vs los picos)

## Gold

```{r}
gold %>% 
  ggplot(., aes(Year, `New York Market Price (U.S. dollars per fine ounce)`))+
  geom_line(size=1)+
  geom_vline(xintercept = 1971, color = "red")+
  geom_label_repel(data=data_frame(),aes(x=1971,y=1000,label="Fin del patrón oro"),nudge_x = -5,force=10,size=7)+
  theme_minimal()+
  labs(y="Dólares por onza de oro", x="Año", title="Precios del oro por onza en el mercado de Nueva York")+
  theme(text = element_text(size = 20))


ggsave("plots/oro.png", dpi=300, width = 10, height = 7, scale=1)
```


## interest_rate


```{r}
graf <- interest_rate %>% 
  gather(type,rate,2:4) %>% 
  ggplot(., aes(Year,rate,color=type))+
  geom_line()+
  guides(color=guide_legend(nrow=2,byrow=TRUE))+
  theme(legend.position = "bottom")
ggplotly(graf) %>%
  layout(legend = list(
      orientation = "h"
    )
  )
```

- La tasa de largo plazo es una serie mucho más suave (eso es un dato conocido de finanzas no?)
- Los surplus funds también parecen ser más volátiles hasta los 40


## sap


```{r}
sap %>% 
  summary()
sap %>% 
  gather(type, value,2:4) %>%
  mutate(type= case_when(type=="The S&P Index Average for January"~"The S&P Index\nAverage for January",
                         type=="The Accumulated S&P Index Average for January"~"The Accumulated S&P\nIndex Average for January",
                         TRUE~type)) %>% 
  ggplot(.,aes(Year,value, color=type))+
  geom_line()+
  facet_grid(type~.,scale="free")+
  theme(legend.position = "bottom",
        strip.text.y = element_text(angle = 0))

```



```{r}
ts(sap$`Annual Yield`, start=min(sap$Year), frequency = 1) %>% 
  na.omit() %>%
  auto.arima(.)

```

## CPI

```{r}

cpi %>% 
  ggplot(., aes(Year, `U.S. Consumer Price Index *`))+
  geom_line(size=1)+
  geom_vline(xintercept = 1971, color = "red")+
  geom_label_repel(data=data_frame(),aes(x=1971,y=100,label="Fin del patrón oro"),nudge_x = -5,force=10,size=7)+
  theme_minimal()+
  labs(y="IPC", x="Año")+
  theme(text = element_text(size = 20))

ggsave("plots/cpi_orig.png", scale = 1)
```

## gdp
```{r}
gdp %>% 
  ggplot(., aes(Year, `Real GDP (millions of 2012 dollars)`))+
  geom_line(size=1)+
  # geom_vline(xintercept = 1971, color = "red")+
  # geom_label_repel(data=data_frame(),aes(x=1971,y=100,label="Fin del patrón oro"),nudge_x = -5,force=10,size=7)+
  theme_minimal()+
  labs(y="Real GDP", x="Año")+
  theme(text = element_text(size = 30))

ggsave("plots/PBI.png", scale = 1)

```



me interesa ver el PBI normalizado por el crecimiento poblacional, y además normalizado por la cantidad de oro que puede comprar (en lugar de normalizar por el CPI):

```{r}


gdp <- left_join(gold, gdp, by = "Year") %>% 
  mutate(gdp_in_gold = `Nominal GDP per capita (current dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) 
  
ggplotly(ggplot(gdp,aes(Year,gdp_in_gold))+
  geom_line())
```

A partir del 1900 pareciera que se arman 3 ciclos muy largos

- 1914-1933
- 1933-1980
- 1980-2012


Agregando referencias históricas de las crisis conocidas

```{r}
library(scales) # to access breaks/formatting functions
ggplot()+
geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-Inf,
            ymax=Inf,
            alpha=0.5)+
  geom_line(size=1,
    data = gdp %>% 
              filter(Year>parse_date_time(1900,"y"))
            ,aes(Year, gdp_in_gold))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")+
  geom_vline(xintercept = parse_date_time(1971,"y"),color = "gold")+ #fin del patron oro
  scale_x_datetime(date_breaks = "15 years",labels = date_format("%Y") )+  
  theme_minimal()+
  labs(x="", y="PBI en oro", title="PBI Estados Unidos",
       subtitle= "Millones de onzas de oro, 1900-2017")+
  theme(text = element_text(size = 20))

ggsave("plots/gdp_in_gold_eda.PNG", dpi = 300, width = 10,height = 6)
```

La guerra de sesesión de EEUU fué entre el 12 de abril de 1861 y el 9 de abril de 1865 

A partir de ahí el pbi en oro crece hasta el fin del patron oro.

## wage

```{r}
wage %>% 
  summary()
```

Podemos deflactar el salario horario por el CPI

```{r}
ggplotly(
wage %>% 
  left_join(cpi,by="Year") %>% 
  na.omit() %>% 
  mutate(salario_horario_real = `Production Workers Hourly Compensation (nominal dollars)`/`U.S. Consumer Price Index *`) %>% 
  ggplot(.,aes(Year,salario_horario_real))+
  geom_line() 
  )
```


El salario real tiene una tendencia creciente hasta el 78' y allí se estanca hasta el 2013



```{r}
wg_gold <- wage %>% 
  filter(Year>=1900) %>% 
  left_join(gold, gdp, by = "Year") %>% 
  mutate(wg_in_gold = `Production Workers Hourly Compensation (nominal dollars)`/`New York Market Price (U.S. dollars per fine ounce)`,
         Year = parse_date_time(Year,"y")) %>% 
  na.omit()  
ggplot()+
geom_rect(data= crisis_largas, 
            aes(xmin=crisis_largas$desde,
            xmax=crisis_largas$hasta),
            fill="firebrick", 
            ymin=-Inf,
            ymax=Inf,
            alpha=0.5)+
  geom_line(size=1,data = wg_gold, aes(Year, wg_in_gold))+
  geom_vline(data=crisis_puntuales, aes(xintercept=desde), color = "red", linetype="dashed")+
  geom_vline(xintercept = parse_date_time(1971,"y"),color = "gold")+ #fin del patron oro
  scale_x_datetime(date_breaks = "15 years",labels = date_format("%Y") )+
  theme_minimal()+  
  labs(x="", y="Salario en oro", title="Salario horario Estados Unidos",
       subtitle="Onzas de oro, 1900-2017")+
  theme(text = element_text(size = 20))


ggsave("plots/wg_in_gold_eda.PNG", dpi = 300, width = 10,height = 6)

```

Se ven los mismos tres períodos. Pero a diferencia del GDP, el período 1980-2012 tiene un nivel más bajo que el anterior. 
 

¿ Si quisieramos comparar ingrsos con algún revenue tendríamos usar S&P o DJA?





#### UK

```{r}
oro <- full_join(brit_gold,gold_london)

names(oro)

```


```{r}

# de http://fx.sauder.ubc.ca/data.html
tc <- read_csv("data/ex_rate.csv")

library(lubridate)

tc <- tc %>% mutate(date = parse_date_time(`MMM YYYY`,"my"),
              Year = year(date)) %>% 
  group_by(Year) %>% 
  summarise(gbp_usd = mean(`GBP/USD`))


# de http://fx.sauder.ubc.ca/etc/USDpages.pdf

tc_1950_1970 <- data_frame(Year=1950:1970, gbp_usd = 0.35714) %>% 
  mutate(gbp_usd = case_when(Year ==1967 ~ 0.36210,
                             Year >1967 ~ 0.41667,
                             TRUE ~ gbp_usd))

tc <- bind_rows(tc_1950_1970,tc)
tc
```



expreso al oro siempre en pounds

```{r}
tail(oro)


oro$`London Market Price (British &pound; [1718-1949] or U.S. $ [1950-2011] per fine ounce)`

oro <- oro %>% 
  filter(Year %in% c(1700:2017)) %>% 
  mutate(serie_unificada = case_when(Year < 1718 ~ `British Official Price (British pounds per fine ounce end of year)`,
                                     Year >=1718 ~`London Market Price (British &pound; [1718-1949] or U.S. $ [1950-2011] per fine ounce)`))

## Tengo que pasar todo a libras, desde 1950 al serie esta en dólares

oro <- oro %>% 
  left_join(tc) %>% 
  mutate(serie_unificada = case_when(Year>1950 ~ serie_unificada*gbp_usd,
                                     TRUE ~ serie_unificada))
  
ggplot(oro, aes(Year, serie_unificada))+
  geom_line()

```


## PBI uk en oro


```{r}

gdp_uk <- gdp_uk %>% 
  left_join(oro) %>% 
  mutate(gdp_in_gold = `Nominal GDP (million of pounds)`/serie_unificada)


crisis <- gdp_uk %>% 
  filter(Year %in% c(1700:1900)) %>% 
  mutate(crisis =gdp_in_gold) %>% 
  filter(Year %in% c(1794,1803, 1812, 1822,1833,1842,1850, 1858, 1868,1879, 1885, 1893))

gdp_uk %>% 
  filter(Year %in% c(1700:1900)) %>% 
ggplot(., aes(Year, gdp_in_gold))+
  geom_line(size=1)+
  geom_text_repel(data = crisis, aes(Year, crisis,label=Year),nudge_x = 5, nudge_y = -20,force=12,size=4)+
  theme_minimal()+
  labs(y="PBI en oro", x="Año", title= "Producto Bruto Interno Reino Unido", 
       subtitle="Millones de onzas de oro. 1700-1900")+
  theme(text = element_text(size = 20))


ggsave("plots/uk_gdp.png",scale = 1)
```


```{r}
gdp_uk %>% 
  write_csv("data/gdp_uk_gold.csv")
```







